Hey Consultants,
Today, let us dive into uncovering Alpha ideas using the Price/Earnings to Growth (PEG) ratio. The PEG ratio is a stock valuation metric that enhances the traditional Price/Earnings (P/E) ratio by incorporating a company's earnings growth rate. This metric helps analysts decide if the market undervalues or overvalues a stock's price relative to its earnings growth prospects.
The basic formula for the PEG ratio is:
PEG = P/E/G
Where:
- ( P/E ) = Price-to-Earnings ratio
- ( G ) = Expected annual earnings growth rate
๐ Components Explained
Price-to-Earnings Ratio (P/E): This is the ratio of a company's current share price to its Earnings Per Share(EPS). It shows how much investors are willing to pay today for a dollar of earnings.
- P/E = Current Stock Price / Earnings Per Share
- Expected Earnings Growth Rate (G): This is the anticipated annual growth rate of the company's earnings. You may estimate it from historical earnings growth, analyst forecasts, or company guidance.
G = (E(t+1) - E(t)) / E(t)
- ( E(t+1) ) = Earnings expected next year
- ( E(t) ) = Earnings this year
The PEG ratio allows you to compare the valuation of companies with different growth rates on an apples-to-apples basis:
๐ First Template
A common approach is to identify stocks with a PEG ratio below 1, which indicates that the stock might be undervalued relative to its growth potential. Conversely, a PEG ratio above 1 could suggest overvaluation.
For example, you can use the following template to create an Alpha that captures relative undervaluation or overvaluation within an industry:
-group_zscore(P / E / G - 1, industry)
This template computes the industry-normalized difference between the PEG-based valuation.
You may find P/E and G from available data fields, or you can calculate them using the above formulas.
Or, you can structure it as:
-<group_compare_op>(<cs_compare_op>(P/E , G), )
Where:
- <cs_compare_op>: Operation to calculate the difference or ratio between PEG ratio and market price (e.g., subtract, divide, vector_neut, regression_neut...)
- <group_compare_op>: Operation to compare within a group (for example: group_zscore, group_rank)
- : The grouping criterion (e.g., industry, sector)
โจ Key Points
- Data Flexibility: The variables (P/E) and (G) can be estimated based on different data sources. You might use historical earnings, analyst estimates, or different models to derive the expected growth rate. Or you may refer to our previous post, where we talked about growth estimation!
- Abstract Operators: The operators and grouping variables are placeholders for the choices that best capture your hypothesis. For instance, <group_compare_op> could be a group_rank if you prefer ranking over z-scores in your comparison.
- Model Assumptions: Remember that the PEG ratio assumes a consistent growth rate, since it is only considering next year's growth, which may not hold true for all companies. It's most applicable to companies with predictable earnings growth.
๐ Second and More
Wait, there is more!
Similar to what we mentioned in previous posts. The advantage of such a financial formula is it provides a basis for comparing valuation metrics across different companies and industries. For example, you can analyze the difference in P/E/G where G is across different forecast period to capture a company's growth prospects.
Can you think of any other Alpha ideas derived from the PEG ratio? Perhaps incorporating adjustments for companies with cyclical earnings or combining forward-looking earnings projections with backward growth rate? Share your innovative ideas and approaches below! ๐ฌ
After reading this, you can understand how to hypothesize based on a well-known financial theory, create an implementation, and test whether it captures any significant signal.
Happy researching! ๐